RESPONSE CALIBRATION FOR NEURAL POPULATION MODELS

Generative models of neural dynamics predict activity well, but whether their learned operators recover the directed effects of perturbations is a separate question. We present PC-Flow, a perturbation-conditioned flow-matching model of population activity whose response operator is calibrated to randomized single-cell interventions. The operator is a finite-dose, stimulated-minus-blank mean contrast, equivalently a dose-averaged input Jacobian of the integrated flow. It estimates directed effective influence through recurrent and hidden pathways, rather than individual synaptic weights. In controlled recurrent circuits, calibration reduces response NRMSE from \CFMResponseError to \PCResponseError, compared with \GaussianCalResponseError for a calibrated Gaussian forecaster and \ResponseNetError for a shared response-only network. Gains persist across the tested recurrence and observation conditions; a mechanism-matched recurrent model remains stronger when the full state is known. Matched objective controls locate the gain in response supervision, while joint predictive scores test the additional distributional information. PC-Flow also predicts held-out simultaneous perturbations: at eight sources, its full-response error is \NonlinearPCError, versus \NonlinearGaussianError for calibrated Gaussian and \NonlinearOracleError for exact-column superposition. Direct interaction-residual tests distinguish nonlinear prediction from improved single-source estimates. These results support response-calibrated generative models as estimators of directed perturbation effects; biological validation remains open.